title: Novel hybrid genetic algorithm for progressive multiple sequence alignment.
abstract: The family of evolutionary or genetic algorithms is used in various fields of bioinformatics. Genetic algorithms (GAs) can be used for simultaneous comparison of a large pool of DNA or protein sequences. This article explains how the GA is used in combination with other methods like the progressive multiple sequence alignment strategy to get an optimal multiple sequence alignment (MSA). Optimal MSA get much importance in the field of bioinformatics and some other related disciplines. Evolutionary algorithms evolve and improve their performance. In this optimisation, the initial pair-wise alignment is achieved through a progressive method and then a good objective function is used to select and align more alignments and profiles. Child and subpopulation initialisation is based upon changes in the probability of similarity or the distance matrix of the alignment population. In this genetic algorithm, optimisation of mutation, crossover and migration in the population of candidate solution reflect events of natural organic evolution.
authors.affiliation: IQRA National University Peshawar, Pakistan.
authors.forename: Muhammad Ishaq
authors.initials: MI
authors.lastname: Afridi
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D019143:Evolution, Molecular; D009154:Mutation; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA; D020539:Sequence Analysis, Protein
keywords: bioinformatics; candidate solution; child population; crossover; distance matrix; evolutionary algorithms; hybrid GAs; hybrid genetic algorithms; migration; mutation; objective function; organic evolution; progressive multiple sequence alignment; similarity probability